Many of the biggest threats to the biosecurity of Australia’s plant industries are bacterial. Difficulties in the identification of phytopathogenic bacteria to the subspecific, or ‘pathovar’, level is difficult and could seriously delay the management of major disease incursions and interfere with market access for plant produce. By definition, pathovars are distinguished by host specificity so bioassays on plants are the definitive means of identification. However, such bioassays require high level physical containment, are slow and subjective and often unreliable.

Rapid serological and molecular tests are available to rapidly and specifically identify some bacteria to pathovar level, but in reality, better diagnostic targets are required to differentiate many exotic pathovars from closely related, endemic organisms. The empirical search for robust markers to pathovar level using classical approaches such as the identification of DNA polymorphisms through sequencing or fingerprinting has been a slow and labour-intensive process and has a poor record of success. Practical limitations are often encountered where assays are based on proteins or genes of unknown function, or regions that are known not to be related to pathogenicity and/or are plasmid-borne.

This project has evaluated proteomic and metabolomic profiling techniques for their ability to fast-track the identification of biomarkers that reliably differentiate particular pathovars from each other. These platforms profile the functional molecules expressed by the organisms, molecules that may be associated with, or even determine, the plant-pathogen interaction. Our hypothesis was that the profiles of two pathovars from the same species would differ because they infect different host plants, and that, as potential determinants of pathogencity, the differentially expressed functional molecules would translate to robust diagnostic markers. The project has used two Xanthomonas species as the model for the work.

The proteomics component of this project has focused on the two-dimensional gel electrophoresis and profiling of membrane-associated proteins extracted from selected bacterial isolates, either grown on media or after passage through sensitive or insensitive plant hosts. Results show that isolates of the same pathovar cluster together and can be differentiated from closely-related pathovars. Proteins that are differentially expressed between operative pairs of pathovars are evident and have been chosen for further analysis by mass spectrometry and peptide sequencing. Reference to genomic sequences has allowed us to identify the genes that encode the differentially expressed proteins. The project is currently developing DNA-based assays from these results and will go on to validate these and deliver the best as improved diagnostic tools for laboratory end-users.

The metabolomics component has analysed metabolite expression in selected bacterial pathovars and in whole plants artificially inoculated with Xanthomonas campestris pathovars. Results show separation between the different pathovars and differentially expressed metabolites, which have potential as new biomarkers, are evident and will be identified and assessed for their ability to provide differentiation at the pathovar level.

This project is the first application of these platforms to plant biosecurity, and the outputs of this project will guide decisions on their potential implementation in fast-tracking the identification of biomarkers to discriminate other species of biosecurity concern, where pathovar differentiation has been a problem.